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<li class="toctree-l1 current"><a class="current reference internal" href="#">Model Comparisons</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#impact-of-time-of-year-and-region">Impact of time of year and region</a></li>
<li class="toctree-l2"><a class="reference internal" href="#comparisons">Comparisons</a></li>
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  <section id="model-comparisons">
<span id="model-examples"></span><h1>Model Comparisons<a class="headerlink" href="#model-comparisons" title="Link to this heading"></a></h1>
<p>The following table shows the different electricity consumption of popular NLP and Computer visions models</p>
<table class="docutils align-center" id="id1">
<caption><span class="caption-text">Electricity consumption of AI cloud instance</span><a class="headerlink" href="#id1" title="Link to this table"></a></caption>
<colgroup>
<col style="width: 25.0%" />
<col style="width: 25.0%" />
<col style="width: 25.0%" />
<col style="width: 25.0%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Model</p></th>
<th class="head"><p>GPU</p></th>
<th class="head"><p>Training Time (H)</p></th>
<th class="head"><p>Consumption (kWh)</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>BERT<sub>fintetune</sub></p></td>
<td><p>4 V100</p></td>
<td><p>6</p></td>
<td><p>3.1</p></td>
</tr>
<tr class="row-odd"><td><p>BERT<sub>pretrain</sub></p></td>
<td><p>8 V100</p></td>
<td><p>36</p></td>
<td><p>37.3</p></td>
</tr>
<tr class="row-even"><td><p>6B<sub>Transf.</sub></p></td>
<td><p>256 A100</p></td>
<td><p>192</p></td>
<td><p>13 812.4</p></td>
</tr>
<tr class="row-odd"><td><p>Dense<sub>121</sub></p></td>
<td><p>1 P40</p></td>
<td><p>0.3</p></td>
<td><p>0.02</p></td>
</tr>
<tr class="row-even"><td><p>Dense<sub>169</sub></p></td>
<td><p>1 P40</p></td>
<td><p>0.3</p></td>
<td><p>0.03</p></td>
</tr>
<tr class="row-odd"><td><p>Dense<sub>201</sub></p></td>
<td><p>1 P40</p></td>
<td><p>0.4</p></td>
<td><p>0.04</p></td>
</tr>
<tr class="row-even"><td><p>ViT<sub>Tiny</sub></p></td>
<td><p>1 V100</p></td>
<td><p>19</p></td>
<td><p>1.7</p></td>
</tr>
<tr class="row-odd"><td><p>ViT<sub>Small</sub></p></td>
<td><p>1 V100</p></td>
<td><p>19</p></td>
<td><p>2.2</p></td>
</tr>
<tr class="row-even"><td><p>ViT<sub>Base</sub></p></td>
<td><p>1 V100</p></td>
<td><p>21</p></td>
<td><p>4.7</p></td>
</tr>
<tr class="row-odd"><td><p>ViT<sub>Large</sub></p></td>
<td><p>4 V100</p></td>
<td><p>90</p></td>
<td><p>93.3</p></td>
</tr>
<tr class="row-even"><td><p>ViT<sub>Huge</sub></p></td>
<td><p>4 V100</p></td>
<td><p>216</p></td>
<td><p>237.6</p></td>
</tr>
</tbody>
</table>
<section id="impact-of-time-of-year-and-region">
<h2>Impact of time of year and region<a class="headerlink" href="#impact-of-time-of-year-and-region" title="Link to this heading"></a></h2>
<p>Carbon emissions that would be emitted from training BERT (language modeling on 8 V100s for 36 hours) in different locations:</p>
<img alt="Models emissions comparison" class="align-center" src="_images/CO2_emitted_BERT.png" />
<p>In this case study, time of year might not be relevant in most cases, but localisation can have a great impact on carbon emissions.</p>
<p>Here, and in the graph below, emissions equivalent are estimated using Microsoft Azure cloud tools.
CodeCarbon has developed its own measuring tools. The result could be different.</p>
</section>
<section id="comparisons">
<h2>Comparisons<a class="headerlink" href="#comparisons" title="Link to this heading"></a></h2>
<p>Emissions for the 11 described models can be displayed as below:</p>
<img alt="Models emissions comparison" class="align-center" src="_images/model_emission_comparison.png" />
<p>The black line represents the average emissions (across regions and time of year).
The light blue represents the first and fourth quartiles.
On the right side, equivalent sources of emissions are displayed as comparison points (source : <a class="reference external" href="https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator">US Environmental Protection Agency</a>).
NB : presented on a log scale</p>
</section>
<section id="references">
<h2>References<a class="headerlink" href="#references" title="Link to this heading"></a></h2>
<p><a class="reference external" href="https://dl.acm.org/doi/10.1145/3531146.3533234">Measuring the Carbon intensity of AI in Cloud Instance</a></p>
<p>Another source comparing models carbon intensity:
<a class="reference external" href="https://arxiv.org/pdf/1906.02243.pdf">Energy and Policy Considerations for Deep Learning in NLP</a></p>
</section>
</section>


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